Patients with Chronic Kidney Disease and Their Intent to Use Electronic Personal Health Records
Bibliographic record
Abstract
BACKGROUND: Electronic personal health records (ePHRs) provide patients with access to their personal health information, aiming to inform them about their health, enhance self-management, and improve outcomes. Although they have been associated with improved health outcomes in several chronic diseases, the potential impact of ePHR use in chronic kidney disease (CKD) is unknown. OBJECTIVES: We sought to understand perceptions of CKD patients about ePHRs, and describe characteristics associated with their expressed intent to use an ePHR. DESIGN: Self-administered paper based survey. SETTING: The study was conducted in Calgary, Alberta, Canada at a multidisciplinary CKD clinic from November 2013 to January 2014. PARTICIPANTS: Patients with non-dialysis-dependent CKD. MEASUREMENTS: Demographics, perceived benefits, and drawbacks of ePHRs were obtained. A univariate analysis was used to assess for an association with the expressed intention to use an ePHR. METHODS: A patient survey was used to determine perceptions of ePHRs, and to identify factors that were associated with intention to use an ePHR. RESULTS: Overall 63 patients with CKD (76.2 % male, 55.6 % ≥65 years old) completed the survey. The majority (69.8 %) expressed their intent to use an ePHR. CKD patients over the age of 65 were less likely to intend to use an ePHR as compared to those aged <65 years (OR 0.22, 95 % CI: [0.06, 0.78]). Those with post-secondary education (OR 3.31, 95 % CI: [1.06, 10.41]) and Internet access (OR 5.70, 95 % CI: [1.64, 19.81]) were more likely to express their intent to use an ePHR. Perceived benefits of ePHR use included greater involvement in their own care (50.0 % indicated this), better access to lab results (75.8 %), and access to health information (56.5 %). Although 41.9 % reported concerns about privacy of health information, there was no association between these concerns and the intent to use an ePHR. LIMITATIONS: Our results are limited by small study size and single centre location. CONCLUSIONS: We found that patients with CKD expressed their intention to use ePHRs, and perceive benefits such as personal involvement in their health care and better access to lab results. Studies of CKD patients using ePHRs are needed to determine whether ePHR use improves patient outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".